Search

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics. [ABSTRACT FROM AUTHOR]

The global transportation sector is rapidly shifting towards electrification, aiming to create more sustainable environments. As a result, there is a significant focus on optimizing performance and increasing the lifespan of batteries in electric vehicles (EVs). To achieve this, the battery pack must operate with constant current charging and discharging modes of operation. Further, in an EV powertrain, maintaining a constant DC link voltage at the input stage of the inverter is crucial for driving the motor load. To satisfy these two conditions simultaneously during the energy transfer, a hybrid energy storage system (HESS) consisting of a lithium–ion battery and a supercapacitor (SC) connected to the semi-active topology of the bidirectional DC–DC converter (SAT-BDC) in this research work. However, generating the duty cycle for the switches to regulate the operation of SAT-BDC is complex due to the simultaneous interaction of the two mentioned constraints: regulating the DC link voltage by tracking the reference and maintaining the battery current at a constant value. Therefore, this research aims to efficiently resolve the issue by incorporating a highly flexible nonlinear model predictive control (NMPC) to control the switches of SAT-BDC. Furthermore, the converter system design is tested for operational performance using MATLAB 2022B with the battery current and the DC link voltage with different priorities. In the NMPC approach, these constraints are carefully evaluated with varying prioritizations, representing a crucial trade-off in optimizing EV powertrain operation. The results demonstrate that battery current prioritization yields better performance than DC link voltage prioritization, extending the lifespan and efficiency of batteries. Thus, this research work further aligns with the conceptual realization of the sustainability goals by minimizing the environmental impact associated with battery production and disposal. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system's responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. [ABSTRACT FROM AUTHOR]

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS. [ABSTRACT FROM AUTHOR]

This paper presents a passivity-based control (PBC) approach integrated with filtering for a supercapacitor (SC) in a semi-active hybrid energy storage system. The PBC is designed as a current controller using the reference provided by the filter to regulate the system's load current. Additionally, an external loop is employed to regulate the SC voltage to a desired value. In this external loop, a low-pass filter is included to decouple voltage and current control during instantaneous changes in load. A detailed, step-by-step description of both the PBC and the SC voltage control strategy is provided, illustrating how voltage regulation is effectively decoupled from current control to ensure optimal operation during load transients. The effectiveness of the proposed control strategy is validated through simulations and Power Hardware-in-the-Loop testing under variable current loads. This comprehensive evaluation method enables testing of control strategies in scenarios closely resembling real-world applications. [ABSTRACT FROM AUTHOR]

Purpose: Supplying off-grid facilities such as astronomical observatories with renewable energy–based systems (RES) instead of diesel generators can considerably reduce their environmental impact. However, RES require oversized capacities to counter intermittency and comply with reliability requirements, hence shifting the environmental impact from operation to construction phase. We assess whether 100% RES scenarios are favorable from an environmental point of view and discuss the trade-offs in systems with backup fossil generators versus 100% renewable ones. Methods: In this comparative life cycle assessment (LCA), we study various RES supply systems to power a new telescope in the Atacama Desert, Chile. We compare six setups, including 100% RES scenarios, namely, photovoltaics (PV) with batteries and hydrogen energy storage; high-renewable scenarios, with fossil fuel power generation next to RES and storage; and a system combining PV with diesel generation. We base system sizing on a techno-economical optimization for the start of operation in 2030. Foreground data stem from life cycle inventories of RES components since 2015 and 2030 electricity mix assumptions of production countries. We assess environmental impact in the categories climate change, mineral resource depletion, and water use. Results and discussion: We find that 100% RES and high-renewable scenarios result in emissions of 0.077–0.115 kg CO2e/kWh supplied, compared to 0.917 kg CO2e/kWh in the reference case with solely diesel generation. One hundred percent RES scenarios have a lower CO2e impact than high-renewable scenarios. However, the latter lower the mineral resource depletion and water use by about 27% compared to 100% RES scenarios. Applying hybrid energy storage systems increases the water use impact while reducing the mineral resource depletion. Conclusions: None of the six energy systems we compared was clearly the best in all environmental impacts considered. Trade-offs must be taken when choosing an energy system to supply the prospective off-grid telescope in Chile. We find high-renewable systems with some fossil generation as the better option regarding power reliability, mineral resource depletion, and water use, while inducing slightly higher greenhouse gas emissions than the 100% RES scenarios. As remote research facilities and off-grid settlements today are mainly supplied by fossil fuels, we expect to motivate more multifaceted decisions for implementing larger shares of RES for these areas. To advance the LCA community in the field of energy systems, we should strive to incorporate temporal and regional realities into our life cycle inventories. To ease the path for upcoming studies, we publish this work's inventories as detailed activity level datasets. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a novel power management strategy (PMS) is proposed for optimal real-time power distribution between battery and supercapacitor hybrid energy storage system in a DC microgrid. The DC-bus voltage regulation and battery life expansion are the main control objectives. Contrary to the previous works that tried to reduce the battery current magnitude and its fluctuation to increase the battery life, the battery thermal considerations, which are a key factor in battery life, are also considered as an objective function in the proposed PMS. Also, the quick charging capability of the battery is another objective in the optimization problem. To solve this multi-objective problem, the Karush–Kuhn–Tucker conditions have been used to make the PMS suitable for real-time operation. In addition, one of the important inputs of conventional PMS is the amount of grid excess/deficiency power. Due to the use of the adaptive Kalman filter estimator, another superior of the proposed PMS is using local data and no need for communication and knowledge of the load or generation power. Using simulation, the performance of the proposed method is compared with three traditional methods filter-based, droop control, and fuzzy logic in terms of controlling the battery current, battery temperature, and battery health, and its superiority has been shown. Finally, the validation of the proposed PMS is experimentally tested on a real setup. [ABSTRACT FROM AUTHOR]

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Abstract The global energy sector is currently undergoing a transformative shift mainly driven by the ongoing and increasing demand for clean, sustainable, and reliable energy solutions. However, integrating renewable energy sources (RES), such as wind, solar, and hydropower, introduces major challenges due to the intermittent and variable nature of RES, affecting grid stability and reliability. Hybrid energy storage systems (HESS), which combine multiple energy storage devices (ESDs), present a promising solution by leveraging the complementary strengths of each technology involved. This comprehensive review examines recent advancements in grid-connected HESS, focusing on their components, design considerations, control strategies, and applications. It provides a detailed analysis of technological progress in various ESDs and the critical role of power conversion, control, energy management, and cooling systems in optimizing HESS performance. Highlighting case studies of some notable and successful HESS implementations across the globe, we illustrate practical applications and identify the benefits and challenges encountered. By addressing these challenges, HESS can significantly enhance the efficiency and reliability of RES, supporting the shift towards a sustainable and resilient energy infrastructure. The paper concludes by identifying future research directions, highlighting the development of intelligent control systems, sustainable materials, and efficient recycling processes to ensure the widespread adoption and long-term viability of HESS.

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology.

This paper proposes a two-layer optimal control strategy for the park power grid, aiming at addressing the energy management challenges arising from the fluctuations in the output of clean energy sources and the random changes in the number of electric vehicles (EV) at the charging station. The upper layer establishes a dynamic optimization model of landscape storage based on the model predictive control technology, which comprehensively incorporates both the battery energy storage system of the park and the battery energy storage system of EVs, enabling them to participate in balancing the energy demand of the power grid, while the lower layer considers meeting the charging demand of EV owners and the vehicle-to-grid management demand of upper layer for energy dispatching simultaneously, and formulates an orderly charging and discharging strategy for EVs in the parking lot of the power grid. The simulation results show that the proposed strategy effectively can integrate scattered EV storage in the park into a unified virtual energy storage system, expand the energy storage capacity of the grid, and increase the consumption of renewable distributed generation supply, ultimately improving economic benefits for the park grid.

Electric propulsion has gained significant popularity in the marine vessel industry due to its numerous advantages. One of the key challenges faced in this domain is effectively managing the fluctuation of power grid caused by sudden load changes. While energy storage systems offer a viable solution, striking the right balance between cost and benefit remains a complex task. To address this issue, establish an optimization model and constraint conditions for capacity configuration of hybrid energy storage systems, and propose a decision-making method based on NSGA-II algorithm and cost-effectiveness method. To address the issue of NSGA-II algorithm being prone to local optima and premature convergence, chaotic thinking and adaptive crossover operators are introduced to improve the NSGA-II algorithm; In order to obtain the optimal configuration scheme from the obtained Pareto optimal solution set, a decision method based on cost-effectiveness was adopted to further analyze the bias between the Pareto optimal solution set and each objective. By comparing the decision results with the ideal solution (TOPSIS), the optimal capacity configuration scheme was obtained.

With the increasing demand for energy storage charging stations, many energy storage systems utilize lithium batteries as the major carriers. However, due to frequent charging and discharging at high power levels, the cycle life of lithium batteries is greatly reduced, which increases the energy storage costs. Given the longevity of supercapacitors, a supercapacitor-lithium hybrid energy storage system has been developed to effectively extend the lifespan of lithium batteries and reduce both investment and operational costs of energy storage charging stations. Based on the dual-stage active topology, a hybrid energy storage system combining supercapacitor-lithium is proposed. Under mild load conditions, two supercapacitor modules are alternatively charged by the lithium battery. Then, the supercapacitor modules are discharges when high power demands are encountered. Accordingly, based on working conditions of the charging pile, a multi-stage strategy, integrating state-of-power estimation and programming, is proposed to optimize the power distribution, smooth the power fluctuation of the lithium battery, and protect the lithium battery. The simulation results show that compared with the lithium batteries only energy storage and the traditional full active topology energy storage, the dual-stage active topology energy storage significantly improves the cycle life of lithium batteries.

The global energy sector is currently undergoing a transformative shift mainly driven by the ongoing and increasing demand for clean, sustainable, and reliable energy solutions. However, integrating renewable energy sources (RES), such as wind, solar, and hydropower, introduces major challenges due to the intermittent and variable nature of RES, affecting grid stability and reliability. Hybrid energy storage systems (HESS), which combine multiple energy storage devices (ESDs), present a promising solution by leveraging the complementary strengths of each technology involved. This comprehensive review examines recent advancements in grid-connected HESS, focusing on their components, design considerations, control strategies, and applications. It provides a detailed analysis of technological progress in various ESDs and the critical role of power conversion, control, energy management, and cooling systems in optimizing HESS performance. Highlighting case studies of some notable and successful HESS implementations across the globe, we illustrate practical applications and identify the benefits and challenges encountered. By addressing these challenges, HESS can significantly enhance the efficiency and reliability of RES, supporting the shift towards a sustainable and resilient energy infrastructure. The paper concludes by identifying future research directions, highlighting the development of intelligent control systems, sustainable materials, and efficient recycling processes to ensure the widespread adoption and long-term viability of HESS. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure. [ABSTRACT FROM AUTHOR]

Improving direct current microgrid (DC-MG) performance is achieved through the implementation in conjunction with a hybrid energy storage system (HESS).The microgrid's operation is optimized by fuzzy logic, which boosts stability and efficiency. By combining many storage technologies, the hybrid energy storage system offers dependable and adaptable energy solutions. The performance of DC microgrids is improved by this combination strategy. In this paper, we proposed the fuzzy logic controller (FLC) and ant colony optimization (ACO) in DC-MG to enhance the performance. The interfaces between the PV, PEMFC, and battery with DC-MG are demonstrated in this research. The approach integrates an achievable phasor alternative from modern power systems. As a result, we evaluate the performance of FLC, ACO, and HESS-DC-MG based on the three layers such as SLGF, DLGF, and TLGF. In SLGF, FLC in the HESS-MG achieved a 98% injection value, surpassing the 97% obtained with ACO. Without HESS-DC-MG, the voltage sag reduction was limited to 75%. In DLGF, FLC in HESS-MG yielded a 97% injection value, while ACO resulted in 95%. Without HESS-DC-MG, the voltage sag reduction was 70%. In TLGF, FLC achieved a 96% injection value, compared to 94% with ACO. The absence of HESS-DC-MG led to a voltage sag reduction of 69%. Overall, HESS-DC-MG technology proves effective in eliminating voltage sag across different scenarios. When comparing these three models, the FLC, ACO, and HESS-DC-MG exhibit superior performance in SLGFS. [ABSTRACT FROM AUTHOR]

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method's effectiveness and superiority. [ABSTRACT FROM AUTHOR]

This paper demonstrates an enhancement of power quality for a photovoltaic (PV) system connected to the grid with a hybrid energy storage system (HESS). The proposed system utilizes a ramp-rate control (RRC) strategy to limit severe fluctuations in the PV power output. Battery storage is integrated to store surplus energy generated by the PV system and is used for continuous power application. A high-power density device, known as a supercapacitor (SC), is employed to mitigate transient fluctuations in the battery. The proposed system facilitates smooth PV power generation, stabilizes the DC bus voltage (VDC), and eliminates source current harmonics induced by non-linear loads. The Shunt Active Power Filter (SAPF) discussed in this paper serves two primary purposes. Firstly, it acts as a reactive power buffer, smoothing out fluctuations and reducing current harmonic distortions. Secondly, it enables active power injection into the grid, utilizing a specific renewable solar PV source. The efficiency of the modeled compensation system is demonstrated by the sinusoidal shape of the current and the compensation of reactive power (RPC). The targeted system showcases the effectiveness of the current setup by exhibiting low total harmonic distortion (THD). The multifunctional features of the proposed system were implemented using the MATLAB/Simulink software, and the results were validated using an OP5700 Hardware-in-the-Loop (HIL) test bench. This integration of distributed power generation capabilities not only enhances the overall power quality but also improves the efficient utilization of renewable energy resources (RESs). [ABSTRACT FROM AUTHOR]

This paper introduces an improved decentralized control strategy for a photovoltaic (PV) hybrid energy storage (HES) system (HESS) in a DC microgrid. The power sharing method of the HES system is discussed in depth. The basic principle of virtual resistance and capacitance droop (VRCD) control, which consists of virtual resistance droop (VRD) and virtual capacitance droop (VCD) control, is analyzed in detail to achieve the decoupling of the HES system for high-and low-frequency load power distribution. For the virtual capacitance control loop, the voltage compensator is added to achieve terminal voltage restoration of the supercapacitor (SC). For the virtual resistance control loop, in order to solve the problems of the unbalanced state of charge (SOC) of battery storage, a virtual resistance droop control based on a novel adaptive function is introduced. Finally, a model of the HESS in a DC microgrid is built in a real-time emulator to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The modern era is witnessing a growing demand for sustainable and eco-friendly power sources. An interconnected power system capable of seamlessly integrating electric vehicles and renewable energy resources is being considered as a viable solution. However, this technology has some drawbacks, such as its lower system inertia, which limits its ability to respond to load capabilities. To overcome this issue, a Hybrid Energy Storage System (HESS) can be integrated with new techniques to enhance performance. This paper proposes a new Quasi Opposition Arithmetic Optimization Algorithm (QOAOA) optimized Fractional Order Proportional Integral Derivative with Filter (FOPIDN) controller cascaded One Plus Tilted Derivative (1 + TD) controller with HESS of super-capacitor (SC) and Redox Flow Battery (RFB) to mitigate frequency and tie-line power variations in a multi-area restructured smart-grid system. The proposed controller's performance is evaluated under various conditions, such as load fluctuations, wind speed variations, solar irradiation, Governor Dead Band (GDB), and generation rate limitations. For unsystematic load the improvement in the performance of the proposed controller indicated by maximum variation in frequency ( Δ f 1 , Δ f 2 , Δ f 3 ) and power change on the tie-lines ( Δ P tie 12 , Δ P tie 13 ) over FOPIDN controller is (98.65%, 48.78%, 52.52%) and (75.0%, 76.1%). The system performance is also analyzed when HESS devices are integrated into the three-area system, taking into account the varying nature of wind speed and solar irradiation. The improvement in performance indicated by maximum variation in frequency ( Δ f 1 , Δ f 2 , Δ f 3 ) and power change on the tie-lines Δ P tie 23 by incorporating HESS in the system is (45.21%, 33.33%, 29.59%) and 46.95% over without energy storage system. The proposed controller's outcomes are validated using real-time hardware-in-the-loop (HIL) simulation with OPAL-RT. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source's performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Electric vehicles operate in a dynamic environment with constantly changing driving conditions, such as varying speeds, terrains, and traffic patterns. Adapting an energy management (EM) strategy to these conditions to maximise efficiency is a significant challenge. Achieving optimal energy management must also consider the cost implications. This manuscript proposes a hybrid technique for the optimum charging capability of electric vehicles (EVs) with a hybrid energy storage system (HESS), such as an electric vehicle, battery, and supercapacitor (SC). The objective is to maximise energy utilisation efficiency, extend the lifespan of energy storage devices, improve overall vehicle performance, and minimise charging costs. The WSO is incorporated to generate the dataset of possible input parameters of HESS. Using the accomplished WSO dataset, the SNN is trained and forecasts the optimum input parameters of Hybrid ESS. The proposed method is implemented in the MATLAB platform and compared with existing methods. The proposed method supercapacitor power is 4.3 $ \times $ × 104W, proposed battery power is 7 $ \times $ × 104 W, supercapacitor current is 90 A and the proposed battery current is 20 A. The proposed method is higher than other existing methods like RFA, WHO and DCNN methods. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery's capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a single-objective function optimization method for the optimal sizing and cost of a hybrid energy storage system (HESS) that integrates lithium-ion batteries (LIB) and supercapacitors (SC) for electric vehicle (EV) applications. The study introduces a comprehensive framework for EV modeling, incorporating simulated EV data to enhance accuracy. A key achievement involves adapting the modified–WLTC driving cycle, iteratively employed in EV simulations to accurately capture the spectrum of power and energy profiles within the designated range, ensuring adherence to BMW-i3's top speed requisites. The proposed method's validity is established by comparing optimization results using Particle Swarm Optimization (PSO) and Firefly Algorithm (FA), indicating comparable HESS sizing and cost outcomes. Notably, the PSO algorithm demonstrates superior accuracy and computational efficiency. Through PSO, the optimal LIB-SC HESS weight is determined at 160 kg with an optimal cost of $27,660, while FA yields 161 kg and $28,270, surpassing LIB-Only EV models by approximately 21 % in improved sizing. This research significantly contributes by establishing a robust EV modeling paradigm, employing simulated data for optimization, and successfully implementing PSO to determine optimal HESS parameters, advancing the field of efficient EV energy storage and promoting cost-effective, sustainable electric transportation.

Lithium-ion battery-based hybrid energy storage systems (ESSs) have been widely applied in various fields. Bidirectional DC/DC converters, crucial interfaces linking batteries and DC buses, serve as critical actuators for tasks such as DC bus regulation, on-line battery diagnosis, health-conscious energy management strategy, and fault tolerant control. Despite their importance, the control of bidirectional DC/DC converters in hybrid ESSs has rarely been independently discussed. Their control-related works are usually carried out by the unidirectional DC/DC converter modeling process with proportional–integral (PI)-type controllers. Therefore, recognizing the significance of pulsed power loads and the induced highly dynamic changes in battery voltage during real-world applications, this paper highlights the relevance of applying H∞-optimal controllers for bidirectional DC/DC converters based on its detailed control-oriented modeling process. To present a rigorous H∞-optimal controller synthesis process, this study includes a detailed experimental determination of the controller’s key parameters. Furthermore, extensive numerical tests across diverse battery operation scenarios have been conducted to demonstrate the advantages of applying H∞-optimal controllers for battery-based bidirectional DC/DC converters in hybrid ESSs.

This paper presents the multiple energy storage system usability for electric motorcycle focused on hybrid topology. This study focuses on evaluating the cost-effectiveness of a hybrid energy storage system (HESS) for e-motorcycles over a 10-year period by considering the number of battery replacements instead of solely the initial system price. The motorcycle studied in this manuscript, is a recreational electric motorcycle utilized as an experimental test bench in EV-SCHS laboratory at the OJEN company. The 10-year equivalent price of the energy storage system includes both the initial purchase cost and the cost of battery replacements over the system's lifespan. A battery life model is essential for estimating the frequency of replacements over the 10-year period. Incorporating ultracapacitors (UC) in the HESS enhances power specifications, leading to improved acceleration and regenerative braking capabilities. This reduces battery current and extends battery life, resulting in fewer required replacements and lower overall system costs over the 10-year period. The study findings demonstrate that optimally-sized HESS configurations offer favorable power specifications, with the 10-year equivalent price of HESS being $3466 lower than that of conventional energy storage systems, despite the initial price of HESS being $833 higher.

Abstract This thesis proposes a charging equalizer for hybrid energy storage systems, incorporating the Zeta converter, voltage multiplier, and multi‐winding transformer. The objective is efficient charging and equalization for battery and supercapacitor strings. The Zeta converter serves to charge the battery string, while the transformer with multi‐winding shares the inductor of Zeta converter as the primary side, facilitating battery string balancing through the utilization of current ripple in the inductor. Additionally, the voltage ripple generated by the Zeta converter is utilized to drive the voltage multiplier, enabling the charging and equalization of the supercapacitor string. The system's operation halts upon reaching the predefined threshold voltage for the supercapacitor strings. The proposed circuit transforms into a Zeta converter by employing a single‐pole double‐throw switch, facilitating the charging of the battery strings. Experimental validation uses four series‐connected supercapacitors and four series‐connected batteries, demonstrating successful equalization and charging for both energy storage components.

Summary: Hybrid energy storage technology, which consists of lithium‐ion batteries (LiB) and super capacitors (SC), is an effective way to ensure the safety of power supply and realize energy saving in metro by reusing the braking power. Aiming at the optimal configuration and control of the metro hybrid energy storage system (HESS), an energy management strategy (EMS) based on dual DC/DC architecture and voltage droop method is proposed. And then the control parameters are adjusted in segments by combining the DC bus voltage and values of SOC of LiB and SC. What's more, a hardware‐in‐loop (HIL) simulation platform of Metro DC power supply system is built to verify the control strategy. The experimental results show that HESS could stabilize the metro voltage within a safe voltage of 580 V and achieve 100% braking energy recovery by optimal energy distribution between two different types of energy storage systems, which are only 79.9% and 39.2% in other single energy storage system by contrast. At the same time, the HIL simulation platform can be used to verify the control effect of different configuration capacities and different control strategies of the HESS, helping to provide the optimal solution of HESS for actual subway lines. [ABSTRACT FROM AUTHOR]

Energy storage systems are essential in modern energy infrastructure, addressing efficiency, power quality, and reliability challenges in DC/AC power systems. Recognized for their indispensable role in ensuring grid stability and seamless integration with renewable energy sources. These storage systems prove crucial for aircraft, shipboard systems, and electric vehicles, addressing peak load demands economically while enhancing overall system reliability and efficiency. Recent advancements and research have focused on high-power storage technologies, including supercapacitors, superconducting magnetic energy storage, and flywheels, characterized by high-power density and rapid response, ideally suited for applications requiring rapid charging and discharging. Hybrid energy storage systems and multiple energy storage devices represent enhanced flexibility and resilience, making them increasingly attractive for diverse applications, including critical loads. This paper provides a comprehensive overview of recent technological advancements in high-power storage devices, including lithium-ion batteries, recognized for their high energy density. In addition, a summary of hybrid energy storage system applications in microgrids and scenarios involving critical and pulse loads is provided. The research further discusses power, energy, cost, life, and performance technologies. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical control method for the power distribution between the hybrid energy storage system (HESS) and the main dc bus of a microgrid for ultrafast charging of electric vehicles (EVs). The HESS is composed of a supercapacitor and a battery and is an essential part to fulfill the charging demand of EVs in a microgrid made up of a 220 V RMS ac bus, two dc buses of 600 V and 1500 V, respectively, and four charging points. A state machine defines the four operating modes of the HESS and establishes the conditions for the corresponding transitions among them, namely, charging the battery and the supercapacitor from the bus, injecting the current from the HESS into the 1500 V dc bus to ensure the power balance in the microgrid, regulating the bus voltage, and establishing the disconnection mode. The primary level of the control system regulates the current and voltage of the battery, supercapacitor, and dc bus, while the secondary level establishes the operating mode of the HESS and provides the appropriate references to the primary level. In the primary level, sliding mode control (SMC) is used in both the battery and supercapacitor in the inner loop of a cascade control that implements the standard constant current–constant voltage (CC-CV) charging protocol. In the same level, linear control is applied in the CV phase of the protocol and for bus voltage regulation or the current injection into the bus. PSIM simulations of the operating modes and their corresponding transitions verify the theoretical predictions. [ABSTRACT FROM AUTHOR]

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind-solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration. [ABSTRACT FROM AUTHOR]

Summary: The peak and transient components of demand power caused by the complex and variable traffic environment could induce the accelerated degradation of the battery lifespan for electric vehicle (EV). This paper proposes a wavelet transform‐based real‐time energy management strategy (EMS) to fully exploit the advantages of the hybrid energy storage system (HESS). First, to adapt the characteristics of battery and ultracapacitor, wavelet transform is employed to decompose driving cycle into high frequency power and low frequency power. Second, since the wavelet transform is difficult to be directly implemented in real‐time control, a power prediction model including four neural network predictors is established, which are trained by the data obtained from wavelet transform to online predict the power of different frequencies. Third, the high frequency power and the low frequency power are distributed to the battery and ultracapacitor by fuzzy logic control (FLC) algorithm, which can significantly reduce the damage caused by current rapid changes into the battery. Accordingly, the battery lifespan is effectively extended because it significantly avoids the impact of rapidly changing and peak current. Finally, simulation results verify the effectiveness of wavelet transform‐based real‐time EMS, and the proposed EMS robustness against temperature is verified at different temperatures. Compared with the wavelet transform strategy (WTS), the peak currents of wavelet transform‐fuzzy logic control strategy (WTFLCS) are decreased by 26.07%, 25.66%, and 25.85% at 10°C, 25°C, and 40°C, respectively. [ABSTRACT FROM AUTHOR]

Supercapacitors attract attention due to their superior values in the parameters like capacitance, discharge currents and cycle lifespan. Supercapacitors are designed and used in many applications where they partially or completely substitute conventional batteries. On the other side, supercapacitors are used in applications which are not so far suitable for these devices. To avoid wrong design and misuse of the supercapacitors it is necessary to correctly understand their properties, key advantages and disadvantages. Similar situation can be found in the field of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

Integrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%. [ABSTRACT FROM AUTHOR]

Power electronic converters are utilized to regulate the charging voltage of electric vehicles (EV) batteries based on photovoltaic (PV), ensuring it falls within the desired range. Nevertheless, multi-port DC-DC converters have encountered challenges like bulky transformers and multiple switches, resulting in reduced reliability. To address these issues, this study presents super lift Luo and buck converter (SLBC) designed for the integration of PV and EV. The DC-DC converter presented in the work, integrated with SLBC, produces both step-up and step-down outputs from single input. The step-up output is achieved through the application of the super-lift method, enabling the elevation of voltage. This method allows for the generation of high-gain voltages using straightforward structures, eliminating the need for additional transformers or electric circuits for control and regulation. For fine tuning the duty cycle of the proposed converter, an efficient control scheme employing a cascaded structure of the TID (tilt integral derivative) with FOPID (fractional order proportional integral derivative with a filter), referred as the cascaded TID-FOPID controller is proposed. The tuning of the cascaded TID-FOPID controller parameters is accomplished using improved Harris Hawks optimization (IHHO). The analysis is carried out in the MATLAB platform and compared to various existing approaches. Analysed parameters include motor torque and speed, converter efficiency across duty cycles (0.1 to 0.6), frequency response, voltage gain comparative analysis among converters at a duty cycle of 0.6, voltage gain, voltage stress, and diode stress comparisons in the proposed converter. The efficiency attained by the proposed method reaches approximately 98%. [ABSTRACT FROM AUTHOR]

Due to environment concerns, Electric Vehicles (EV) are becoming more and more common in the automobile industry. Since rechargeable batteries of EV manage power, it is crucial to have a battery charger that is dependable, efficient, and affordable in order to provide the battery of the specific EV with the stable required output. Due to the expanding market for renewable energy combined with EV, converters have received a lot of attention recently. Because it reduces system losses and transformer winding losses, non-isolated DC/DC converters are suitable for EV applications. Other problems with the non-isolated DC/DC converter include inadequate voltage gain, a high duty cycle ratio, and the requirement for additional circuitry for better performance. To achieve reliable control of converters, a combination of an optimization technique with Tilt Integral Derivative (TID) controller is used in the Non-Isolated High Step-Up (NIHSU) DC/DC converter for Solar Photo Voltaic (SPV) integrated with EV Applications are proposed. The proposed system TID tuned by Dung Beetle Optimizer are executed in the MATLAB platform and compared with various approaches in terms of voltage regulation, efficiency. The proposed methodology improves closed-loop systems functionality and achieves significant voltage increase, greater power density and higher efficiency of 97.8%. [ABSTRACT FROM AUTHOR]

This thesis proposes a charging equalizer for hybrid energy storage systems, incorporating the Zeta converter, voltage multiplier, and multi‐winding transformer. The objective is efficient charging and equalization for battery and supercapacitor strings. The Zeta converter serves to charge the battery string, while the transformer with multi‐winding shares the inductor of Zeta converter as the primary side, facilitating battery string balancing through the utilization of current ripple in the inductor. Additionally, the voltage ripple generated by the Zeta converter is utilized to drive the voltage multiplier, enabling the charging and equalization of the supercapacitor string. The system's operation halts upon reaching the predefined threshold voltage for the supercapacitor strings. The proposed circuit transforms into a Zeta converter by employing a single‐pole double‐throw switch, facilitating the charging of the battery strings. Experimental validation uses four series‐connected supercapacitors and four series‐connected batteries, demonstrating successful equalization and charging for both energy storage components. [ABSTRACT FROM AUTHOR]

Aiming to solve the problems of insufficient dynamic responses, the large loss of energy storage life of a single power cell, and the large fluctuation in DC (direct current) bus voltage in fuel cell vessels, this study takes a certain type of fuel cell ferry as the research object and proposes an improved equivalent minimum hydrogen consumption energy management strategy, based on fuzzy logic control. First, a hybrid power system including a fuel cell, a lithium–iron–phosphate battery, and a supercapacitor is proposed, with the simulation of the power system of the modified mother ship. Second, a power system simulation model and a double-closed-loop PI (proportion integration) control model are established in MATLAB/Simulink to design the equivalent hydrogen consumption model and fuzzy logic control strategy. The simulation results show that, under the premise of meeting the load requirements, the control strategy designed in this paper improves the Li-ion battery's power, the Li-ion battery's SOC (state of charge), the bus voltage stability, and the equivalent hydrogen consumption significantly, compared with those before optimization, which improves the stability and economy of the power system and has certain practical engineering value. [ABSTRACT FROM AUTHOR]

Copyright of Advances in New & Renewable Energy is the property of Editorial Office of Advances in New & Renewable Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)